Version 1
: Received: 13 April 2020 / Approved: 15 April 2020 / Online: 15 April 2020 (10:02:51 CEST)
Version 2
: Received: 16 April 2020 / Approved: 17 April 2020 / Online: 17 April 2020 (08:48:55 CEST)
Version 3
: Received: 9 May 2020 / Approved: 10 May 2020 / Online: 10 May 2020 (15:14:11 CEST)
Version 4
: Received: 15 May 2020 / Approved: 16 May 2020 / Online: 16 May 2020 (16:13:16 CEST)
Version 5
: Received: 20 May 2020 / Approved: 21 May 2020 / Online: 21 May 2020 (04:13:13 CEST)
Version 6
: Received: 14 July 2020 / Approved: 15 July 2020 / Online: 15 July 2020 (03:14:33 CEST)
How to cite:
Shuler, R.L.; Koukouvitis, T.; Suematsu, D. Partial Unlock for COVID-19-Like Epidemics Can Save 1-3 Million Lives Worldwide. Preprints2020, 2020040239. https://doi.org/10.20944/preprints202004.0239.v4
Shuler, R.L.; Koukouvitis, T.; Suematsu, D. Partial Unlock for COVID-19-Like Epidemics Can Save 1-3 Million Lives Worldwide. Preprints 2020, 2020040239. https://doi.org/10.20944/preprints202004.0239.v4
Shuler, R.L.; Koukouvitis, T.; Suematsu, D. Partial Unlock for COVID-19-Like Epidemics Can Save 1-3 Million Lives Worldwide. Preprints2020, 2020040239. https://doi.org/10.20944/preprints202004.0239.v4
APA Style
Shuler, R.L., Koukouvitis, T., & Suematsu, D. (2020). Partial Unlock for COVID-19-Like Epidemics Can Save 1-3 Million Lives Worldwide. Preprints. https://doi.org/10.20944/preprints202004.0239.v4
Chicago/Turabian Style
Shuler, R.L., Theodore Koukouvitis and Dyske Suematsu. 2020 "Partial Unlock for COVID-19-Like Epidemics Can Save 1-3 Million Lives Worldwide" Preprints. https://doi.org/10.20944/preprints202004.0239.v4
Abstract
This paper accounts in lives-saved partial unlock strategies that may be used to facilitate reopening economies that have been shut down due to an epidemic or pandemic. For this purpose it introduces a new approach to simulation using an internal SIR engine with seasonality, and external behavior forcing calibrated with case data to account for initial human behavior under social distancing. The overall method relies on public goal setting and both professional and public feedback behavior. In this way it avoids much of the chaotic sensitivity to parameters and divergence of predictions and behavior which undermine the public image of epidemiology models and create rebounds. We study reducing the total cases by controlling threshold overshoot as economies reopen, controlling medical resource utilization, and reducing economic shutdown duration, all of these across significant scenario variation. We provide a quantitative analysis of overshoot and demonstrate a two-step manual method as well as the feedback method of avoiding it. We show goal-managed partial unlock to manage critical resources has the consequential effects of reducing economic downtime and bringing the cumulative cases down about 9%-27%, thereby saving lives with some degree of certainty. The optimization of overshoot does leave some risk of creating a residual small infection existing on birth rate and migration, and we provide some guidelines for minimizing the risk. Effectiveness is demonstrated using COVID-19 actual data and parameters for other diseases with replication factors up to 15.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
16 May 2020
Commenter:
Robert Shuler
Commenter's Conflict of Interests:
Author
Comment:
Updated simulator includes seasonality, more accurate. Analysis of hypothetical cases other than COVID-19. Tighter focus on what's new in this research. Rewrite sections 3 and 4 for compactness. Slight re-wording of title. Added Suematsu as co-author.
Commenter: Robert Shuler
Commenter's Conflict of Interests: Author